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import numpy as np
import torch
from os.path import join as pjoin
from .humanml.utils.word_vectorizer import WordVectorizer
from .humanml.scripts.motion_process import (process_file, recover_from_ric)
from .HumanML3D import HumanML3DDataModule
from .humanml import Text2MotionDatasetEval, Text2MotionDataset, Text2MotionDatasetCB, MotionDataset, MotionDatasetVQ, Text2MotionDatasetToken
class KitDataModule(HumanML3DDataModule):
def __init__(self, cfg, **kwargs):
super().__init__(cfg, **kwargs)
# Basic info of the dataset
self.name = "kit"
self.njoints = 21
# Path to the dataset
data_root = cfg.DATASET.KIT.ROOT
self.hparams.data_root = data_root
self.hparams.text_dir = pjoin(data_root, "texts")
self.hparams.motion_dir = pjoin(data_root, 'new_joint_vecs')
# Mean and std of the dataset
dis_data_root = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 'kit',
"VQVAEV3_CB1024_CMT_H1024_NRES3", "meta")
self.hparams.mean = np.load(pjoin(dis_data_root, "mean.npy"))
self.hparams.std = np.load(pjoin(dis_data_root, "std.npy"))
# Mean and std for fair evaluation
dis_data_root_eval = pjoin(cfg.DATASET.KIT.MEAN_STD_PATH, 't2m',
"Comp_v6_KLD005", "meta")
self.hparams.mean_eval = np.load(pjoin(dis_data_root_eval, "mean.npy"))
self.hparams.std_eval = np.load(pjoin(dis_data_root_eval, "std.npy"))
# Length of the dataset
self.hparams.max_motion_length = cfg.DATASET.KIT.MAX_MOTION_LEN
self.hparams.min_motion_length = cfg.DATASET.KIT.MIN_MOTION_LEN
self.hparams.max_text_len = cfg.DATASET.KIT.MAX_TEXT_LEN
self.hparams.unit_length = cfg.DATASET.KIT.UNIT_LEN
# Get additional info of the dataset
self._sample_set = self.get_sample_set(overrides={"split": "test", "tiny": True})
self.nfeats = self._sample_set.nfeats
cfg.DATASET.NFEATS = self.nfeats
def feats2joints(self, features):
mean = torch.tensor(self.hparams.mean).to(features)
std = torch.tensor(self.hparams.std).to(features)
features = features * std + mean
return recover_from_ric(features, self.njoints)
def joints2feats(self, features):
features = process_file(features, self.njoints)[0]
# mean = torch.tensor(self.hparams.mean).to(features)
# std = torch.tensor(self.hparams.std).to(features)
# features = (features - mean) / std
return features
def normalize(self, features):
mean = torch.tensor(self.hparams.mean).to(features)
std = torch.tensor(self.hparams.std).to(features)
features = (features - mean) / std
return features
def renorm4t2m(self, features):
# renorm to t2m norms for using t2m evaluators
ori_mean = torch.tensor(self.hparams.mean).to(features)
ori_std = torch.tensor(self.hparams.std).to(features)
eval_mean = torch.tensor(self.hparams.mean_eval).to(features)
eval_std = torch.tensor(self.hparams.std_eval).to(features)
features = features * ori_std + ori_mean
features = (features - eval_mean) / eval_std
return features
def mm_mode(self, mm_on=True):
# random select samples for mm
if mm_on:
self.is_mm = True
self.name_list = self.test_dataset.name_list
self.mm_list = np.random.choice(self.name_list,
self.cfg.METRIC.MM_NUM_SAMPLES,
replace=False)
self.test_dataset.name_list = self.mm_list
else:
self.is_mm = False
self.test_dataset.name_list = self.name_list
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